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-driven mechanical characterization of solid and porous materials, supported by an NSF CMMI CDS&E award and industry-sponsored projects. The primary function of the position is to develop and cross
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Associate in the Peng Research Group . The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials
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Associate in the Peng Research Group . The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials
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Researchers in AI-driven atomistic modeling and AI-accelerated cheminformatics to join the Otaniemi Center for Atomic-scale Materials Modeling (OCAMM), hosted by the Department of Chemistry and Materials
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Associate in the Peng Research Group . The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials
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materials; materials modeling; magnetic; 2D materials; tight-binding; ferroelectrics; high-throughput Eligibility citizenship Open to U.S. citizens level Open to Postdoctoral applicants Stipend Base
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systems, or HVAC alternatives IoT and sensor networks in the built environment AI or data-driven modeling and control for building and urban systems Human-centered or interaction design for smart systems
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quantification Extend the modeling framework to porous and cellular materials, such as polymer and metal foams, including compressible constitutive formulations, calibration against experimental data, and
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, Applied Mathematics, or a related field. Strong expertise in: Computer vision and image processing Deep learning (e.g., PyTorch, TensorFlow) Data-driven modeling and analysis Proficiency in Python and
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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Data-driven materials discovery and design Location Material Measurement Laboratory